README.md

---
base_model: microsoft/mpnet-base
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100000
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: People on bicycles waiting at an intersection.
  sentences:
  - More than one person on a bicycle is obeying traffic laws.
  - The people are on skateboards.
  - People waiting at a light on bikes.
- source_sentence: A dog is in the water.
  sentences:
  - A white dog with brown spots standing in water.
  - A woman in a white outfit holds her purse while on a crowded bus.
  - A wakeboarder is traveling across the water behind a ramp.
- source_sentence: The people are sleeping.
  sentences:
  - A man and young boy asleep in a chair.
  - A father and his son cuddle while sleeping.
  - Several people are sitting on the back of a truck outside.
- source_sentence: A dog is swimming.
  sentences:
  - A brown god relaxes on a brick sidewalk.
  - The furry brown dog is swimming in the ocean.
  - a black dog swimming in the water with a tennis ball in his mouth
- source_sentence: A dog is swimming.
  sentences:
  - A woman in all black throws a football indoors while man looks at his cellphone
    in the background.
  - A white dog with a stick in his mouth standing next to a black dog.
  - A dog with yellow fur swims, neck deep, in water.
model-index:
- name: MPNet base trained on AllNLI triplets
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli dev
      type: all-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9059842041312273
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.09386391251518833
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.900820170109356
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9017314702308628
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9059842041312273
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli test
      type: all-nli-test
    metrics:
    - type: cosine_accuracy
      value: 0.9185958541382963
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.08019367529126949
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9142078983204721
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9142078983204721
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9185958541382963
      name: Max Accuracy
---

# MPNet base trained on AllNLI triplets

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("korruz/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
    'A dog is swimming.',
    'A dog with yellow fur swims, neck deep, in water.',
    'A white dog with a stick in his mouth standing next to a black dog.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Triplet
* Dataset: `all-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value     |
|:-------------------|:----------|
| cosine_accuracy    | 0.906     |
| dot_accuracy       | 0.0939    |
| manhattan_accuracy | 0.9008    |
| euclidean_accuracy | 0.9017    |
| **max_accuracy**   | **0.906** |

#### Triplet
* Dataset: `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9186     |
| dot_accuracy       | 0.0802     |
| manhattan_accuracy | 0.9142     |
| euclidean_accuracy | 0.9142     |
| **max_accuracy**   | **0.9186** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### sentence-transformers/all-nli

* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 100,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         | negative                                                   |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code>             |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
|:-----:|:----:|:-------------:|:------------------------:|:-------------------------:|
| 0     | 0    | -             | 0.6832                   | -                         |
| 0.032 | 100  | 3.2593        | 0.8010                   | -                         |
| 0.064 | 200  | 1.318         | 0.8152                   | -                         |
| 0.096 | 300  | 1.2552        | 0.8256                   | -                         |
| 0.128 | 400  | 1.3322        | 0.8141                   | -                         |
| 0.16  | 500  | 1.4141        | 0.8224                   | -                         |
| 0.192 | 600  | 1.2339        | 0.8149                   | -                         |
| 0.224 | 700  | 1.2556        | 0.8091                   | -                         |
| 0.256 | 800  | 1.138         | 0.8262                   | -                         |
| 0.288 | 900  | 1.0928        | 0.8311                   | -                         |
| 0.32  | 1000 | 1.0438        | 0.8341                   | -                         |
| 0.352 | 1100 | 1.1159        | 0.8323                   | -                         |
| 0.384 | 1200 | 1.1909        | 0.8472                   | -                         |
| 0.416 | 1300 | 1.2542        | 0.8543                   | -                         |
| 0.448 | 1400 | 1.2359        | 0.8574                   | -                         |
| 0.48  | 1500 | 1.0265        | 0.8712                   | -                         |
| 0.512 | 1600 | 0.8688        | 0.8783                   | -                         |
| 0.544 | 1700 | 0.8819        | 0.8841                   | -                         |
| 0.576 | 1800 | 0.8903        | 0.8931                   | -                         |
| 0.608 | 1900 | 0.9334        | 0.8858                   | -                         |
| 0.64  | 2000 | 1.0225        | 0.9028                   | -                         |
| 0.672 | 2100 | 0.9252        | 0.9034                   | -                         |
| 0.704 | 2200 | 0.9036        | 0.9033                   | -                         |
| 0.736 | 2300 | 0.8122        | 0.9040                   | -                         |
| 0.768 | 2400 | 0.8503        | 0.9058                   | -                         |
| 0.8   | 2500 | 0.8448        | 0.9055                   | -                         |
| 0.832 | 2600 | 0.7918        | 0.9039                   | -                         |
| 0.864 | 2700 | 0.7787        | 0.9025                   | -                         |
| 0.896 | 2800 | 0.8624        | 0.9034                   | -                         |
| 0.928 | 2900 | 0.9513        | 0.9058                   | -                         |
| 0.96  | 3000 | 0.6548        | 0.9072                   | -                         |
| 0.992 | 3100 | 0.0163        | 0.9060                   | -                         |
| 1.0   | 3125 | -             | -                        | 0.9186                    |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->